Recommender systems are well known in the art. In one example, such systems can make recommendations for movie titles to a subscriber. In other instances they can provide suggestions for book purchases, or even television program viewing. Such algorithms are commonplace in a number of Internet commerce environments, including at Amazon, CDNOW, and Netflix to name a few, as well as programming guide systems such as TiVO.
Traditionally recommender systems are used in environments in which a content provider is attempting to provide new and interesting material to subscribers, in the form of additional products and services. In some cases (see e.g., U.S. Pat. No. 6,493,703 incorporated by reference herein) recommenders have been employed for the purpose of informing members of an online community of content and/or preferences of other members. Nonetheless the use of recommenders has not been extended fully to such domains and other online areas, including social networks, which could benefit from such systems. Only recently for example have recommenders been proposed for generating user to user recommendations in a music related community. See e.g., U.S. Publication No. 2007/0203790 to Torrens, incorporated by reference herein. Similar systems which recommend content/users are described in U.S. Pat. No. 6,493,703 to Knight et al., also incorporated by reference herein.
Multi-dimensional recommenders have also been recently introduced. For an example of such systems, please see U.S. Patent Publication No. 2004/0103092 to Tuzhilin et al. and an article entitled “Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach” to Adomavicius et al., both of which are hereby incorporated by reference herein. In such systems, however, the extra dimensionality arises from additional content related to items which are nonetheless still traditional commerce items, such as movies.